Facade defects classification from imbalanced dataset using meta learning-based convolutional neural network

被引:62
|
作者
Guo, Jingjing [1 ]
Wang, Qian [1 ]
Li, Yiting [2 ]
Liu, Pengkun [3 ]
机构
[1] Natl Univ Singapore, Sch Design & Environm, Dept Bldg, Singapore 117566, Singapore
[2] Natl Univ Singapore, Fac Engn, Dept Elect & Comp Engn, Singapore, Singapore
[3] Chongqing Univ, Sch Civil Engn, Chongqing, Peoples R China
关键词
DYNAMIC CLASSIFICATION; DAMAGE DETECTION;
D O I
10.1111/mice.12578
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Facade inspection is a regular but necessary maintenance task to ensure the safety, functioning, and aesthetics of a building. Traditional visual identification of facade defects is dangerous, time-consuming, and insufficient. Based on an image dataset and deep learning algorithms, an automatic facade defects classification technique is developed in this research. A layer-based categorization rule is proposed to categorize facade defects. To handle the problem of imbalanced data size among defect classes, a meta learning-based method is applied, which reassigns weights to the training data. Experiments demonstrated that the proposed method had a stronger capacity to deal with the imbalanced dataset problem comparing with previous methods by improving the classification accuracy from 71.43% of a basic convolutional neural network (CNN) model to 82.86% of a meta learning-based CNN model.
引用
收藏
页码:1403 / 1418
页数:16
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